• Grazing Cow Behavior’s Association with Mild and Moderate Lameness

      O’Leary, Niall W.; Byrne, Daire. T.; Garcia, Pauline; Werner, Jessica; Cabedoche, Morgan; Shalloo, Laurence; Science Foundation Ireland; Department of Agriculture, Food and Marine; 13/IA/1977; 16/RC/3835 (MDPI AG, 2020-04-11)
      Accelerometer-based mobility scoring has focused on cow behaviors such as lying and walking. Accuracy levels as high as 91% have been previously reported. However, there has been limited replication of results. Here, measures previously identified as indicative of mobility, such as lying bouts and walking time, were examined. On a research farm and a commercial farm, 63 grazing cows’ behavior was monitored in four trials (16, 16, 16, and 15 cows) using leg-worn accelerometers. Seventeen good mobility (score 0), 23 imperfect mobility (score 1), and 22 mildly impaired mobility (score 2) cows were monitored. Only modest associations with activity, standing, and lying events were found. Thus, behavior monitoring appears to be insufficient to discern mildly and moderately impaired mobility of grazing cows.
    • Invited review: Cattle lameness detection with accelerometers

      O'Leary, N.W.; Byrne, D.T.; O'Connor, A.H.; Shalloo, Laurence; Science Foundation Ireland; 13/IA/1977 (American Dairy Science Association, 2020-05)
      Locomotion scoring is time consuming and is not commonly completed on farms. Farmers also underestimate their herds' lameness prevalence, a knowledge gap that impedes lameness management. Automation of lameness detection could address this knowledge gap and facilitate improved lameness management. The literature pertinent to adding lameness detection to accelerometers is reviewed in this paper. Options for lameness detection systems are examined including the choice of sensor, raw data collected, variables extracted, and statistical classification methods used. Two categories of variables derived from accelerometer-based systems are examined. These categories are behavior measures such as lying and measures of gait. For example, one measure of gait is the time a leg is swinging during a gait cycle. Some behavior-focused studies have reported accuracy levels of greater than 80%. Cow gait measures have been investigated to a lesser extent than behavior. However, classification accuracies as high as 91% using gait measures have been reported with hardware likely to be practical for commercial farms. The need for even higher accuracy and potential barriers to adoption are discussed. Significant progress is still required to realize a system with sufficient specificity and sensitivity. Lameness detection systems using 1 accelerometer per cow and a resolution lower than 100 Hz with gait measurement functions are suggested to balance cost and data requirements. However, gait measurement using accelerometers is rather underdeveloped. Therefore, a high priority should be given to the development of novel gait measures and testing their ability to differentiate lame from nonlame cows.
    • Validation of an ear tag–based accelerometer system for detecting grazing behavior of dairy cows

      Pereira, G.M.; Heins, B.J.; O'Brien, Bernadette; McDonagh, A.; Lidauer, L.; Kickinger, F.; USDA National Institute of Food and Agriculture; Science Foundation Ireland; 2012-51300-20015 (Elsevier, 2020-02-20)
      The objective of the study was to develop a grazing algorithm for an ear tag–based accelerometer system (Smartbow GmbH, Weibern, Austria) and to validate the grazing algorithm with data from a noseband sensor. The ear tag has an acceleration sensor, a radio chip, and temperature sensor for calibration and it can monitor rumination and detect estrus and localization. To validate the ear tag, a noseband sensor (RumiWatch, Itin and Hoch GmbH, Liestal, Switzerland) was used. The noseband sensor detects pressure and acceleration patterns, and, with a software program specific to the noseband, pressure and acceleration patterns are used to classify data into eating, ruminating, drinking, and other activities. The study was conducted at the University of Minnesota West Central Research and Outreach Center (Morris, MN) and at Teagasc Animal and Grassland Research and Innovation Centre (Moorepark, Fermoy, Co. Cork, Ireland). During May and June 2017, observational data from Minnesota and Ireland were used to develop the grazing algorithm. During September 2018, data were collected by the ear tag and noseband sensor from 12 crossbred cows in Minnesota for a total of 248 h and from 9 Holstein-Friesian cows in Ireland for a total of 248 h. A 2-sided t-test was used to compare the percentage of grazing and nongrazing time recorded by the ear tag and the noseband sensor. Pearson correlations and concordance correlation coefficients (CCC) were used to evaluate associations between the ear tag and noseband sensor. The percentage of total grazing time recorded by the ear tag and by the noseband sensor was 37.0% [95% confidence interval (CI): 32.1 to 42.0] and 40.5% (95% CI: 35.5 to 45.6), respectively, in Minnesota, and 35.4% (95% CI: 30.6 to 40.2) and 36.9% (95% CI: 32.1 to 41.8), respectively, in Ireland. The ear tag and noseband sensor agreed strongly for monitoring grazing in Minnesota (r = 0.96; 95% CI: 0.94 to 0.97, CCC = 0.95) and in Ireland (r = 0.92; 95% CI: 0.90 to 0.94, CCC = 0.92). The results suggest that there is potential for the ear tag to be used on pasture-based dairy farms to support management decision-making.